Tomographic reconstruction is an important inverse problem in a wide range of imaging systems, including medical scanners, explosive detection systems and electron and X-ray microscopy for scientific and materials imaging. The objective of tomographic reconstruction is to compute a three-dimensional volume (a physical object or a scene) from two-dimensional observations that are acquired using an imaging system. An example of tomographic reconstruction is found in computed tomography (CT) scans, in which X-ray radiation is passed from several angles to record 2D radiographic images of specific parts of the scanned patient. These radiographic images are then processed using a reconstruction algorithm to form a 3D volumetric view of the scanned region, which is subsequently used for medical diagnosis.
Model Based Iterative Reconstruction (MBIR) is a promising approach to realize tomographic reconstruction. The MBIR framework formulates the problem of reconstruction as minimization of a high-dimensional cost function, in which each voxell in the 3D volume is a variable. An iterative algorithm is employed to optimize the cost function such that a pre-specified error threshold is met.
MBIR has demonstrated state-of-the art reconstruction quality on various applications and has been utilized commercially in GE's healthcare systems. In addition to improved image quality, MBIR has enabled significant reduction in X-ray dosage in the context of lung cancer screening (˜80% reduction) and pediatric imaging (30-50% reduction). In other application domains, MBIR offers additional advantages such as improved output resolution, precise definition with reduced impact of undesired artifacts in images, and the ability to reconstruct even with sparse view angles. These capabilities are extremely critical in applications such as explosive detection systems (e.g. baggage and cargo scanners), where there is a need to reduce cost due to false alarm rates, operate under non-ideal view angles, and extend deployed systems to cover new threat scenarios.
While MBIR shows great potential, its high compute and data requirements are key bottlenecks to its widespread commercial adoption. For instance, reconstructing a 512×512×256 volume of nanoparticles viewed from different angles through an electron microscope requires 50.33 GOPS (Giga operations) and 15 G memory accesses per iteration of MBIR. Further, the algorithm may take 10s of iterations to converge depending on the threshold. Clearly, this places significant compute demand. One tested software implementation required ˜1700 seconds per iteration on a 2.3 GHz AMD Opteron server with 196 GB memory, which is unacceptable for many practical applications. Thus, technologies that enable orders of magnitude improvement in MBIR's implementation efficiency are needed.